The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed from modern machines such as Medical Resonance (MR) imaging scanners, Computed Tomographic (CT) scanners and Positron Emission Tomographic (PET) scanners, to multimodality imaging systems such as PET-CT and PET-MRI systems. Because of large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, MRI, etc. Digital medical images are typically either a two-dimensional (“2D”) image made of pixel elements, a three-dimensional (“3D”) image made of volume elements (“voxels”) or a four-dimensional (“4D”) image made of dynamic elements (“doxels”). Such 2D, 3D or 4D images are processed using medical image recognition techniques to determine the presence of anatomical abnormalities such as cysts, tumors, polyps, etc. Given the amount of image data generated by any given image scan, it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or condition.
Recognizing anatomical structures within digitized medical images presents multiple challenges. For example, a first concern relates to the accuracy of recognition of anatomical structures within an image. A second area of concern is the speed of recognition. Because medical images are an aid for a doctor to diagnose a disease or condition, the speed with which an image can be processed and structures within that image recognized can be of the utmost importance to the doctor in order to reach an early diagnosis. Hence, there is a need for improving recognition techniques that provide accurate and fast recognition of anatomical structures and possible abnormalities in medical images.
Automatic image processing and recognition of structures within a medical image is generally referred to as Computer-Aided Detection (CAD). A CAD system can process medical images and identify anatomical structures including possible abnormalities for further review. Such possible abnormalities are often called candidates and are considered to be generated by the CAD system based upon the medical images.
Head and neck vessel imaging using MR provides valuable information for the diagnosis of stenosis, dissection, aneurysms and vascular tumors. In order to achieve suitable imaging qualities in contrast enhanced or non-contrast enhanced magnetic resonance angiography (MRA), high-resolution MR slices should be positioned at a specific location and orientation with respect to specific arterial or venous vessels. For example, both carotid arteries, including aortic arch and circle of Willis, should be covered by high-resolution coronal slices. Further, additional scout slices may be acquired to facilitate positioning of so-called Combined Applications to Reduce Exposure (CARE) bolus or test-bolus slices. The last two help to reliably meet the optimal time point of the contrast-agent bolus arrival in the region of interest (ROI).
Proper slice positioning is time consuming, and the number of slices is directly related to the acquisition time and temporal or spatial resolution of dynamic angiographies. Additionally, the slice orientation can also influence the presence of artifacts in the resulting images (e.g., wrap-around if field-of-view is too small). Multiple repetitions to obtain proper positioning and imaging results need to be avoided, particularly in time-critical or emergency examinations (e.g., in stroke MR examinations) and due to the fact that contrast agent administration cannot be repeated during the same MR examination. Thus, slice positioning needs to cover the relevant anatomical structures with the least number of slices and to achieve optimal imaging results. However, in current workflows, slice positioning is often a bottleneck in increasing the speed of workflow and reliability across operators.